{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:06:49Z","timestamp":1766138809210,"version":"3.41.0"},"reference-count":78,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>A fundamental challenge of recommender systems (RS) is understanding the causal dynamics underlying users\u2019 decision making. Most existing literature addresses this problem by using causal structures inferred from domain knowledge. However, there are numerous phenomenons where domain knowledge is insufficient, and the causal mechanisms must be learned from the feedback data. Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users\u2019 exposure and their willingness to interact. Also for this reason, most existing solutions become inappropriate since they require data collected free from any RS.<\/jats:p>\n          <jats:p>\n            In this article, we first formulate the underlying causal mechanism as a causal structural model and describe\n            <jats:italic>CSL4RS<\/jats:italic>\n            , a general causal structure learning framework for RS grounded in the real-world working mechanism. The essence of our approach is to acknowledge the unknown nature of RS intervention. We then derive the learning objective from our framework and utilize an augmented Lagrangian solver for efficient optimization. We conduct both simulation and real-world experiments to demonstrate how our approach compares favorably to existing solutions, together with the empirical analysis from sensitivity and ablation studies.\n          <\/jats:p>","DOI":"10.1145\/3680296","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T11:28:27Z","timestamp":1722425307000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Causal Structure Learning for Recommender System"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0865-5223","authenticated-orcid":false,"given":"Shuyuan","family":"Xu","sequence":"first","affiliation":[{"name":"Computer Science, Rutgers University, New Brunswick, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7599-2815","authenticated-orcid":false,"given":"Da","family":"Xu","sequence":"additional","affiliation":[{"name":"LinkedIn, Sunnyvale, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7754-3652","authenticated-orcid":false,"given":"Evren","family":"Korpeoglu","sequence":"additional","affiliation":[{"name":"Walmart Labs, Sunnyvale, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5643-5263","authenticated-orcid":false,"given":"Sushant","family":"Kumar","sequence":"additional","affiliation":[{"name":"Walmart Labs, Sunnyvale, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5054-2850","authenticated-orcid":false,"given":"Stephen","family":"Guo","sequence":"additional","affiliation":[{"name":"Indeed, Sunnyvale, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9186-3175","authenticated-orcid":false,"given":"Kannan","family":"Achan","sequence":"additional","affiliation":[{"name":"Walmart Labs, Sunnyvale, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2633-8555","authenticated-orcid":false,"given":"Yongfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Science, Rutgers University, New Brunswick, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Invariant risk minimization","author":"Arjovsky Martin","year":"2019","unstructured":"Martin Arjovsky, L\u00e9on Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. 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